Event based text mining for integrated network construction

Yvan Saeys, Sofie Van Landeghem, Yves Van Peer
Proceedings of the third International Workshop on Machine Learning in Systems Biology, PMLR 8:112-121, 2009.

Abstract

The scientific literature is a rich and challenging data source for research in systems biology, providing numerous interactions between biological entities. Text mining techniques have been increasingly useful to extract such information from the literature in an automatic way, but up to now the main focus of text mining in the systems biology field has been restricted mostly to the discovery of protein-protein interactions. Here, we take this approach one step further, and use machine learning techniques combined with text mining to extract a much wider variety of interactions between biological entities. Each particular interaction type gives rise to a separate network, represented as a graph, all of which can be subsequently combined to yield a so-called integrated network representation. This provides a much broader view on the biological system as a whole, which can then be used in further investigations to analyse specific properties of the network.

Cite this Paper

BibTeX
@InProceedings{pmlr-v8-saeys10a, title = {Event based text mining for integrated network construction}, author = {Saeys, Yvan and Landeghem, Sofie Van and Peer, Yves Van}, booktitle = {Proceedings of the third International Workshop on Machine Learning in Systems Biology}, pages = {112--121}, year = {2009}, editor = {Džeroski, Sašo and Guerts, Pierre and Rousu, Juho}, volume = {8}, series = {Proceedings of Machine Learning Research}, address = {Ljubljana, Slovenia}, month = {05--06 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v8/saeys10a/saeys10a.pdf}, url = {https://proceedings.mlr.press/v8/saeys10a.html}, abstract = {The scientific literature is a rich and challenging data source for research in systems biology, providing numerous interactions between biological entities. Text mining techniques have been increasingly useful to extract such information from the literature in an automatic way, but up to now the main focus of text mining in the systems biology field has been restricted mostly to the discovery of protein-protein interactions. Here, we take this approach one step further, and use machine learning techniques combined with text mining to extract a much wider variety of interactions between biological entities. Each particular interaction type gives rise to a separate network, represented as a graph, all of which can be subsequently combined to yield a so-called integrated network representation. This provides a much broader view on the biological system as a whole, which can then be used in further investigations to analyse specific properties of the network.} }
Endnote
%0 Conference Paper %T Event based text mining for integrated network construction %A Yvan Saeys %A Sofie Van Landeghem %A Yves Van Peer %B Proceedings of the third International Workshop on Machine Learning in Systems Biology %C Proceedings of Machine Learning Research %D 2009 %E Sašo Džeroski %E Pierre Guerts %E Juho Rousu %F pmlr-v8-saeys10a %I PMLR %P 112--121 %U https://proceedings.mlr.press/v8/saeys10a.html %V 8 %X The scientific literature is a rich and challenging data source for research in systems biology, providing numerous interactions between biological entities. Text mining techniques have been increasingly useful to extract such information from the literature in an automatic way, but up to now the main focus of text mining in the systems biology field has been restricted mostly to the discovery of protein-protein interactions. Here, we take this approach one step further, and use machine learning techniques combined with text mining to extract a much wider variety of interactions between biological entities. Each particular interaction type gives rise to a separate network, represented as a graph, all of which can be subsequently combined to yield a so-called integrated network representation. This provides a much broader view on the biological system as a whole, which can then be used in further investigations to analyse specific properties of the network.
RIS
TY - CPAPER TI - Event based text mining for integrated network construction AU - Yvan Saeys AU - Sofie Van Landeghem AU - Yves Van Peer BT - Proceedings of the third International Workshop on Machine Learning in Systems Biology DA - 2009/03/02 ED - Sašo Džeroski ED - Pierre Guerts ED - Juho Rousu ID - pmlr-v8-saeys10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 8 SP - 112 EP - 121 L1 - http://proceedings.mlr.press/v8/saeys10a/saeys10a.pdf UR - https://proceedings.mlr.press/v8/saeys10a.html AB - The scientific literature is a rich and challenging data source for research in systems biology, providing numerous interactions between biological entities. Text mining techniques have been increasingly useful to extract such information from the literature in an automatic way, but up to now the main focus of text mining in the systems biology field has been restricted mostly to the discovery of protein-protein interactions. Here, we take this approach one step further, and use machine learning techniques combined with text mining to extract a much wider variety of interactions between biological entities. Each particular interaction type gives rise to a separate network, represented as a graph, all of which can be subsequently combined to yield a so-called integrated network representation. This provides a much broader view on the biological system as a whole, which can then be used in further investigations to analyse specific properties of the network. ER -
APA
Saeys, Y., Landeghem, S.V. & Peer, Y.V.. (2009). Event based text mining for integrated network construction. Proceedings of the third International Workshop on Machine Learning in Systems Biology, in Proceedings of Machine Learning Research 8:112-121 Available from https://proceedings.mlr.press/v8/saeys10a.html.

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